Extracting metadata from the netCDF files
Fadi Moubayed
9/14/2020
Loading packages
# JSON libraries
library(rjson)
library(RJSONIO)
library(jsonlite)
# To read the netCDF file (check for better options!)
library(ncdf4)
# To get the min bounding box
library(sp)
library(shotGroups)
# To plot the track of the glider
library(spacetime)
library(trajectories)
library(leaflet)
# To format dates
library(lubridate)
# ?? Not sure why I was using this
library(tidyverse)The following function lists the functions available in the loaded libraries
ListLoadedLibraries = function(){
# Listing the default libraries. These do not need to be listed
defaultLibraries = list("stats","graphics" ,"grDevices","utils","datasets" ,"methods","base")
# Listing the loaded libraries
loadedLibraries = (.packages())
# Getting the loaded libraries only
LoadedlibrariesOnly = setdiff(loadedLibraries, defaultLibraries)
# Adding package: to the names of the libraries
libraries1= paste0("package:", LoadedlibrariesOnly)
sapply(libraries1, ls)
}Listing what functions the JSON libraries in R have
# ListLoadedLibraries()The following function extracts the minimum bounding box of the netCDF file into a json file
getMinBBox = function(filePath){
}Opening the netCDF file and getting the lattitude and longitude
# This is where the netCDF data files reside
netCDF_Data_Files_Path = "D:/University/WWU/WWU 5/Task 1/Reading netCDF data using R/"
# This is the files that needs to be read
netCDF_File_Name = "amerigo_coconet_R.nc"
# This is the complete path to the netCDF file that is being read
file_Path = paste0(netCDF_Data_Files_Path,netCDF_File_Name)
#Reading the file
file = nc_open(file_Path)
# Getting the latitude and longitude
lon = ncvar_get(file,"LONGITUDE")
lat = ncvar_get(file,"LATITUDE")Getting the min bounding box using the library sp
# Combining the lat and lon in a dataframe
lat_lon = cbind.data.frame(lat,lon)
# Changing the column names of the dataframe
names(lat_lon)[1] = "point.x"
names(lat_lon)[2] = "point.y"
# Converting the dataframe to a SpatialPoints object
lat_lon_spatial = SpatialPoints(lat_lon)
# Applying the bbox function
bb_sp = bbox(lat_lon_spatial)
head(lat_lon)## point.x point.y
## 1 41.66751 17.06750
## 2 41.66751 17.06751
## 3 41.66751 17.06751
## 4 41.66751 17.06752
## 5 41.66752 17.06753
## 6 41.66752 17.06753bb_sp## min max
## point.x 41.32905 41.80003
## point.y 16.90610 17.67666Getting the bbox using the library shotGroups
This function also returns the width and height of the bounding box. Since the projection is WGS84. This information is not useful.
bb = getBoundingBox(lat_lon)
bb## $pts
## xleft ybottom xright ytop
## 41.32905 16.90610 41.80003 17.67666
##
## $width
## [1] 0.4709773
##
## $height
## [1] 0.7705615
##
## $FoM
## [1] 0.6207694
##
## $diag
## [1] 0.9030973Getting the minimum and the maximum of the lat and lon
This is to check what the functions above are returning.
range(lat)## [1] 41.32905 41.80003range(lon)## [1] 16.90610 17.67666Getting the minimum bounding box
This function returns the minimum bounding box but it returns NULL. I was not able to figure out what the problem is.
x = getMinBBox(lat_lon)
x## NULLtest = cbind(lat,lon)
y = getMinBBox(test)
y## NULLPlotting the track of the netCDF file
# Getting the time
time_D = file$dim$TIME
# Formatting the time
time_D_formatted = as.POSIXct(time_D$vals, origin="1970-01-01")
# Setting a projection
crs = CRS("+proj=longlat +datum=WGS84")# Creating an STIDF object
stidf = STIDF(SpatialPoints(cbind(lat,lon),crs), time_D_formatted, data.frame(lat_lon))
# Creating a track object
glider_track = Track(stidf)
plot(glider_track)
# Accessing the coordinates of the Track object
# A1@data[,1]
# A1@data[,2]
# Ploting the glider track using leflet
leaflet() %>%addTiles() %>% addPolylines(lat = glider_track@data[,1], lng = glider_track@data[,2])# Getting the time stamp of the time available in the netCDF file
timeStamp = format(time_D_formatted,'%H:%M:%S')
# Getting the date of the time available in the netCDF file
date = as.Date(time_D_formatted)
# Combining the lat, lon, time , time stamp and the date in one dataframe
lat_lon_time_dateframe = cbind.data.frame(lat, lon, time_D_formatted, timeStamp, date)
head(lat_lon_time_dateframe)## lat lon time_D_formatted timeStamp date
## 1 41.66751 17.06750 2013-05-15 14:14:48 14:14:48 2013-05-15
## 2 41.66751 17.06751 2013-05-15 14:14:53 14:14:53 2013-05-15
## 3 41.66751 17.06751 2013-05-15 14:14:53 14:14:53 2013-05-15
## 4 41.66751 17.06752 2013-05-15 14:14:58 14:14:58 2013-05-15
## 5 41.66752 17.06753 2013-05-15 14:15:02 14:15:02 2013-05-15
## 6 41.66752 17.06753 2013-05-15 14:15:03 14:15:03 2013-05-15# !!! I tried this but it does not work !!!
# I wanted to get the minimum date of each day in the dataframe
# test %>% group_by(time_D_formatted) %>% filter(time_D_formatted == min(time_D_formatted))Getting the first date of each day through looping the time column
This is not effecient at all and R crashed when I tried it.
time_array = lat_lon_time_dateframe[,3]
head(time_array)## [1] "2013-05-15 14:14:48 CEST" "2013-05-15 14:14:53 CEST"
## [3] "2013-05-15 14:14:53 CEST" "2013-05-15 14:14:58 CEST"
## [5] "2013-05-15 14:15:02 CEST" "2013-05-15 14:15:03 CEST"#Getting the first value of the arry
first_value = time_array[1]
as.Date(ymd_hms(first_value))## [1] "2013-05-15"#for(i in time_array){
# as.Date(ymd_hms(i))
#}Trying to add the coordinated of the labels that should be added to the leaflet map manually
Adding the coordinates to the addMarkers using coordinate1[1] did not work. I had to hard code the coordinates.
This section extracts the coordinates of the first value of each day manually. The labels are also manually added to the leaflet map.
# manual_labels = data.frame("lat", "lon", "time_formated")
coordinate1 = c(as.double(41.66751), as.double(17.06750), "2013-05-15 14:14:48")
coordinate2 = c("41.69140", "17.09850", "2013-05-16 02:00:00")
leaflet() %>%addTiles() %>% addPolylines(lat = glider_track@data[,1], lng = glider_track@data[,2]) %>% addMarkers(lng = 17.06750,
lat = 41.66751,
label = coordinate1[3],
labelOptions = labelOptions(noHide =T)) %>% addMarkers(lng = 17.09850, lat = 41.69140, label = coordinate2[3])Getting the first value of each day of the data available in the netCDF file
What I thought of is splitting the dataframe that has all the days in the netCDF file into seperate dataframes each containing the measurement for each day. The first row of each dataframe is then extracted to get the first measurement of that day.
# Splitting the dataframe based on date
# This results in a list of dataframes
list = split(lat_lon_time_dateframe, lat_lon_time_dateframe$date)
#This provides the first row of each day in the data frame
first_days = do.call(rbind, (lapply(list, function(x) x[1,])))
# Plotting the glider track with labels
leaflet() %>%addTiles() %>% addPolylines(lat = glider_track@data[,1], lng = glider_track@data[,2]) %>% addMarkers(data = first_days, lat = ~first_days$lat, lng = ~first_days$lon, label = first_days$time_D_formatted)# This provides a summary of the dataframe based on the date
lat_lon_time_dateframe %>% group_split(date) %>% map(summary)## [[1]]
## lat lon time_D_formatted
## Min. :41.67 Min. :17.05 Min. :2013-05-15 14:14:49
## 1st Qu.:41.68 1st Qu.:17.06 1st Qu.:2013-05-15 17:54:43
## Median :41.69 Median :17.07 Median :2013-05-15 20:17:24
## Mean :41.68 Mean :17.07 Mean :2013-05-15 20:12:52
## 3rd Qu.:41.69 3rd Qu.:17.08 3rd Qu.:2013-05-15 22:31:33
## Max. :41.69 Max. :17.10 Max. :2013-05-16 01:59:56
##
## timeStamp date
## 00:02:58: 2 Min. :2013-05-15
## 00:03:53: 2 1st Qu.:2013-05-15
## 00:04:13: 2 Median :2013-05-15
## 00:04:28: 2 Mean :2013-05-15
## 00:04:53: 2 3rd Qu.:2013-05-15
## 00:05:33: 2 Max. :2013-05-15
## (Other) :6359
##
## [[2]]
## lat lon time_D_formatted
## Min. :41.67 Min. :17.10 Min. :2013-05-16 02:00:01
## 1st Qu.:41.68 1st Qu.:17.11 1st Qu.:2013-05-16 07:31:51
## Median :41.70 Median :17.11 Median :2013-05-16 13:54:56
## Mean :41.71 Mean :17.11 Mean :2013-05-16 14:14:17
## 3rd Qu.:41.74 3rd Qu.:17.12 3rd Qu.:2013-05-16 21:08:16
## Max. :41.74 Max. :17.12 Max. :2013-05-17 01:59:59
##
## timeStamp date
## 00:01:02: 2 Min. :2013-05-16
## 00:01:12: 2 1st Qu.:2013-05-16
## 00:02:27: 2 Median :2013-05-16
## 00:02:37: 2 Mean :2013-05-16
## 00:04:02: 2 3rd Qu.:2013-05-16
## 00:04:12: 2 Max. :2013-05-16
## (Other) :14895
##
## [[3]]
## lat lon time_D_formatted
## Min. :41.74 Min. :16.96 Min. :2013-05-17 02:00:04
## 1st Qu.:41.77 1st Qu.:16.98 1st Qu.:2013-05-17 08:14:57
## Median :41.77 Median :17.02 Median :2013-05-17 13:58:50
## Mean :41.77 Mean :17.02 Mean :2013-05-17 14:10:15
## 3rd Qu.:41.78 3rd Qu.:17.05 3rd Qu.:2013-05-17 20:31:23
## Max. :41.79 Max. :17.10 Max. :2013-05-18 01:59:59
##
## timeStamp date
## 00:00:02: 2 Min. :2013-05-17
## 00:00:12: 2 1st Qu.:2013-05-17
## 00:01:27: 2 Median :2013-05-17
## 00:01:37: 2 Mean :2013-05-17
## 00:01:47: 2 3rd Qu.:2013-05-17
## 00:03:12: 2 Max. :2013-05-17
## (Other) :19265
##
## [[4]]
## lat lon time_D_formatted
## Min. :41.76 Min. :16.91 Min. :2013-05-18 02:00:01
## 1st Qu.:41.78 1st Qu.:16.91 1st Qu.:2013-05-18 07:23:55
## Median :41.78 Median :16.92 Median :2013-05-18 13:21:59
## Mean :41.78 Mean :16.93 Mean :2013-05-18 13:31:38
## 3rd Qu.:41.80 3rd Qu.:16.94 3rd Qu.:2013-05-18 19:25:51
## Max. :41.80 Max. :16.97 Max. :2013-05-19 01:59:56
##
## timeStamp date
## 00:00:25: 2 Min. :2013-05-18
## 00:00:40: 2 1st Qu.:2013-05-18
## 00:01:15: 2 Median :2013-05-18
## 00:02:25: 2 Mean :2013-05-18
## 00:03:05: 2 3rd Qu.:2013-05-18
## 00:04:10: 2 Max. :2013-05-18
## (Other) :20255
##
## [[5]]
## lat lon time_D_formatted
## Min. :41.61 Min. :16.95 Min. :2013-05-19 02:00:01
## 1st Qu.:41.64 1st Qu.:17.02 1st Qu.:2013-05-19 07:59:35
## Median :41.70 Median :17.08 Median :2013-05-19 13:56:07
## Mean :41.69 Mean :17.09 Mean :2013-05-19 14:00:28
## 3rd Qu.:41.72 3rd Qu.:17.15 3rd Qu.:2013-05-19 19:56:06
## Max. :41.76 Max. :17.23 Max. :2013-05-20 01:59:56
##
## timeStamp date
## 00:00:36: 2 Min. :2013-05-19
## 00:00:46: 2 1st Qu.:2013-05-19
## 00:02:01: 2 Median :2013-05-19
## 00:02:11: 2 Mean :2013-05-19
## 00:03:26: 2 3rd Qu.:2013-05-19
## 00:03:36: 2 Max. :2013-05-19
## (Other) :21596
##
## [[6]]
## lat lon time_D_formatted
## Min. :41.47 Min. :17.23 Min. :2013-05-20 02:00:01
## 1st Qu.:41.51 1st Qu.:17.29 1st Qu.:2013-05-20 07:46:55
## Median :41.54 Median :17.34 Median :2013-05-20 13:13:58
## Mean :41.54 Mean :17.34 Mean :2013-05-20 13:49:13
## 3rd Qu.:41.57 3rd Qu.:17.39 3rd Qu.:2013-05-20 20:14:30
## Max. :41.61 Max. :17.43 Max. :2013-05-21 01:59:57
##
## timeStamp date
## 00:00:54: 2 Min. :2013-05-20
## 00:01:04: 2 1st Qu.:2013-05-20
## 00:02:19: 2 Median :2013-05-20
## 00:02:29: 2 Mean :2013-05-20
## 00:03:44: 2 3rd Qu.:2013-05-20
## 00:03:54: 2 Max. :2013-05-20
## (Other) :21571
##
## [[7]]
## lat lon time_D_formatted
## Min. :41.36 Min. :17.43 Min. :2013-05-21 02:00:02
## 1st Qu.:41.39 1st Qu.:17.48 1st Qu.:2013-05-21 07:18:33
## Median :41.41 Median :17.52 Median :2013-05-21 12:45:18
## Mean :41.41 Mean :17.53 Mean :2013-05-21 13:45:00
## 3rd Qu.:41.43 3rd Qu.:17.59 3rd Qu.:2013-05-21 20:29:33
## Max. :41.47 Max. :17.63 Max. :2013-05-22 01:59:57
##
## timeStamp date
## 00:00:56: 2 Min. :2013-05-21
## 00:02:21: 2 1st Qu.:2013-05-21
## 00:03:46: 2 Median :2013-05-21
## 00:04:31: 2 Mean :2013-05-21
## 00:04:41: 2 3rd Qu.:2013-05-21
## 00:05:56: 2 Max. :2013-05-21
## (Other) :19689
##
## [[8]]
## lat lon time_D_formatted
## Min. :41.33 Min. :17.63 Min. :2013-05-22 02:00:02
## 1st Qu.:41.34 1st Qu.:17.65 1st Qu.:2013-05-22 03:26:50
## Median :41.34 Median :17.66 Median :2013-05-22 04:33:02
## Mean :41.34 Mean :17.66 Mean :2013-05-22 04:36:56
## 3rd Qu.:41.35 3rd Qu.:17.67 3rd Qu.:2013-05-22 05:39:05
## Max. :41.36 Max. :17.68 Max. :2013-05-22 07:04:49
##
## timeStamp date
## 02:00:31: 2 Min. :2013-05-22
## 02:01:26: 2 1st Qu.:2013-05-22
## 02:02:11: 2 Median :2013-05-22
## 02:02:26: 2 Mean :2013-05-22
## 02:03:16: 2 3rd Qu.:2013-05-22
## 02:24:50: 2 Max. :2013-05-22
## (Other) :4640